All Outputs (6)

Augmented Reality AI Co-Driver: Impact on Drivers Perceived Experience and Safety (2023)
Presentation / Conference Contribution
Charissis, V. (2023, December). Augmented Reality AI Co-Driver: Impact on Drivers Perceived Experience and Safety. Presented at 30th International Display Workshop, Niigata, Japan

This project investigates the use of an AI codriver that could support the driver's decision-making process. The information is presented through AR HUD and audio. The evaluation by 20 users in a VR driving simulator presented both encouraging outcom... Read More about Augmented Reality AI Co-Driver: Impact on Drivers Perceived Experience and Safety.

Use and operational safety (2023)
Book Chapter
Reed, N., Charisis, V., & Cowper, S. (2023). Use and operational safety. In D. Ventriglia, & M. Kahl (Eds.), FISITA Intelligent Safety White Paper – The Safety of Electro-Mobility: Expert considerations on the Safety of an Electric Vehicle from concept through end of life (107-111). FISITA

Whether you are an individual buying your first car or replacing an existing vehicle, or if you are a fleet manager making vehicle purchase decisions on behalf of a company, the acquisition of a car is usually a highly significant purchase. Increasi... Read More about Use and operational safety.

A stacking ensemble of deep learning models for IoT intrusion detection (2023)
Journal Article
Lazzarini, R., Tianfield, H., & Charissis, V. (2023). A stacking ensemble of deep learning models for IoT intrusion detection. Knowledge-Based Systems, 279, Article 110941. https://doi.org/10.1016/j.knosys.2023.110941

The number of Internet of Things (IoT) devices has increased considerably in the past few years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a consequence, the prompt detection of attacks in IoT environments thr... Read More about A stacking ensemble of deep learning models for IoT intrusion detection.

Electric Vehicles Safety Issues and Concerns (2023)
Preprint / Working Paper
Reed, N., Charissis, V., & Cowper, S. (in press). Electric Vehicles Safety Issues and Concerns. FISTA

Federated Learning for IoT Intrusion Detection (2023)
Journal Article
Lazzarini, R., Tianfield, H., & Charissis, V. (2023). Federated Learning for IoT Intrusion Detection. Artificial Intelligence, 4(3), 509-530. https://doi.org/10.3390/ai4030028

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems... Read More about Federated Learning for IoT Intrusion Detection.

A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection (2023)
Preprint / Working Paper
Lazzarini, R., Tianfield, H., & Charissis, V. A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection

The number of Internet of Things (IoT) devices has increased considerably inthe past few years, which resulted in an exponential growth of cyber attackson IoT infrastructure. As a consequence, the prompt detection of attacks inIoT environments throug... Read More about A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection.